Systems and methods for controlling online advertising campaigns

ABSTRACT

Systems and methods are provided for controlling an online advertising campaign. In one embodiment, a computer-implemented method for controlling an online advertising campaign includes receiving a feedback signal reflecting delivery of the online advertising campaign, and comparing the feedback signal  t o a delivery reference to generate a campaign level control signal. The method further includes receiving a maximum impression bid price for an inventory unit of the online advertising campaign, the maximum bid price for the at least one inventory unit being set by a user, and calculating, using at least one processor, at least a final bid price based on the maximum bid price, on the campaign level control signal, and on an optimization objective for the online advertising campaign, the optimization objective being set by the user. The method also includes submitting, to an electronic market and based on the calculated final bid price, a bid on an impression from the inventory unit.

TECHNICAL FIELD

The present disclosure relates generally to online advertising andcomputerized systems and methods for operating and controlling the same.More specifically, and without limitation, the present disclosurerelates to systems and methods for controlling one or more aspectsrelated to an online advertising campaign, such as bid price and bidallocation.

BACKGROUND

Internet advertisers often create online advertising campaigns thatinclude numerous advertisements (e.g., “banner ads”) designed to beplaced on websites during a specified period of time. For example, acompany may design several advertisements for a product, and may wish tohave the advertisements placed on websites during the sale of theproduct. Each time one of these advertisements is shown to a websitevisitor is known as an “impression.” When shown the advertisement, theuser may select, r “click,” on the advertisement or may take another“action” such as completing an online form to request more information.If the user later purchases the product, the purchase is referred to asa “conversion” of the impression.

Advertisers may be interested in impressions (e.g., if they are tryingto increase awareness of a brand), clicks (e.g., if they are trying toprovide more information about a product), or conversions (e.g., if theyare trying to make sales or get new users to sign up for services).Advertisers may pay based on, for example, impressions, clicks, rconversions over the course of an advertising campaign. An advertisermay have a spending plan that specifies how the advertiser wishes tospend its budget during a campaign. For example, the advertiser may wishto spend money only on certain days during the campaign, or may wish tospend evenly over every day of the campaign. Each advertiser may have adaily budget (e.g., $1,000 per day) and/or a daily goal of impressionvolume (e.g., 1000 impressions per day), known as “daily delivery” or“pacing.” Each advertiser may also desire an ad campaign to performcertain types of consumer targeting and/or achieve a particulardistribution of advertisements across various websites (“spreading”).

As a result, various techniques are used to manage online ad deliverywithin and among advertising campaigns. In certain advertising networks,ad servers receive impression requests from publishers (e.g., websites rother entities with an inventory of online ad space). The ad servers maygroup ad requests from various advertising campaigns, e.g., according toimpressions to be “targeted” based on a combination of attributesdefined by the ad requests. In addition to targeting requirements, eachad request received from an advertiser generally includes a “bid price”and possibly a “bid allocation”. The bid price is the amount of moneyoffered by the ad request for placement of the targeted impression. Thebid allocation, if present, is the ratio (e.g., point value from 0 to 1)of targeted inventory the ad campaign is willing to purchase at the bidprice. The list of ads that target a certain impression request may besorted in descending order according to their bid price, and then placedin groups such that the sum of their bid allocations equals 1. If the adrequest with the highest bid price has a bid allocation of 1, or if thebid allocation does not exist, it will always win the impression. Suchad delivery methods ensure that the advertiser with an ad with thegreatest expected value is able to purchase as much inventory asdesired. These methods also reveal both the marginal value of theimpression inventory (e.g., the cost required per impression), and theamount of volume (e.g., the number of impressions) purchased at eachprice.

This type of competitive bidding marketplace may be problematic,however, from the perspective of the user (i.e., advertiser). Forexample, while conventional online advertising campaigns may controldelivery in a way that balances advertisers' return on investment withpublishers' revenue, they do not necessary optimize performance in favorof the user. Additionally, while ad networks may be aware of thetechniques used to control campaign delivery, such techniques are notnecessarily apparent to the user.

SUMMARY

In accordance with one disclosed exemplary embodiment, a system isprovided for controlling an online advertising campaign. The system mayinclude a sensor configured to provide a feedback signal reflecting adelivery of the online advertising campaign. The system may furtherinclude a campaign controller configured to compare the feedback signalto a delivery reference and to generate a campaign level control signalbased on the comparison. The system may further include an actuator. Theactuator may be configured to receive a maximum impression bid price foran inventory unit of the online advertising campaign, the maximum bidprice for the at least one inventory unit being set by a user. Theactuator may be further configured to calculate at least a final bidprice based on the maximum bid price, on the campaign level controlsignal, and on an optimization objective for the online advertisingcampaign, the optimization objective being selected by the user.Additionally, the system may submit, to an electronic market and basedon the calculated final bid price, a bid on an impression from theinventory unit.

In accordance with another disclosed exemplary embodiment, acomputer-implemented method is provided for controlling an onlineadvertising campaign. The method may include receiving a feedback signalreflecting a delivery of the online advertising campaign, and comparingthe feedback signal to a delivery reference to generate a campaign levelcontrol signal. The method may further include receiving a maximumimpression bid price for an inventory unit of the Nine advertisingcampaign, the maximum bid price for the at least one inventory unitbeing set by a user. The method may further include calculating, usingat least one processor, at least a final bid price based on the maximumbid price, on the campaign level control signal, and on an optimizationobjective for the online advertising campaign, the optimizationobjective being selected by the user. The method may also includesubmitting, to an electronic market and based on the calculated finalbid price, a bid on an impression from the inventory unit.

In accordance with another disclosed exemplary embodiment, acomputer-implemented method is provided for controlling an onlineadvertising campaign. This method may include receiving a feedbacksignal reflecting a delivery of the online advertising campaign, andcomparing the feedback signal to a delivery reference to generate acampaign level control signal. The method may further include receivinga maximum impression bid price for an inventory unit of the onlineadvertising campaign, the maximum bid price for the at least oneinventory unit being set by a user, detecting ad events associated withthe inventory unit and determining an ad event rate based on thedetected ad events, and calculating a performance value for theinventory unit based on the ad event rate. Additionally, the method mayinclude calculating, using at least one processor, a final bid pricebased on the maximum bid price, on the performance value, on thecampaign level control signal, and on an optimization objective for theonline advertising campaign that is selected by the user. Them methodmay further include submitting, to an electronic market and based on thecalculated final bid price, a bid on an impression from the inventoryunit.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram of an exemplary system in which onlineadvertising campaigns may be controlled, consistent with embodiments ofthe present disclosure;

FIG. 2 illustrates an exemplary control system for controlling onlineadvertising campaigns in an advertising network, consistent withembodiments of the present disclosure;

FIG. 3 illustrates a flow diagram of an exemplary method for controllingonline advertising campaigns, consistent with embodiments of the presentdisclosure;

FIG. 4 depicts a block diagram of an exemplary system for controllingonline advertising campaigns, consistent with embodiments of the presentdisclosure;

FIG. 5A illustrates an exemplary embodiment of a unit controller foradjusting a bid allocation, consistent with embodiments of the presentdisclosure;

FIG. 5B illustrates an exemplary embodiment of a unit controller foradjusting a bid price and/or bid allocation, consistent with embodimentsof the present disclosure;

FIG. 6 illustrates an exemplary embodiment of a unit controller forcontrolling online advertising campaigns, consistent with embodiments ofthe present disclosure;

FIG. 7 illustrates an exemplary embodiment of a unit actuator in ainventory unit actuation environment, consistent with the presentdisclosure;

FIG. 8 illustrates a flow diagram of an exemplary method for controllingonline advertising campaigns, consistent with embodiments of the presentdisclosure;

FIG. 9 illustrates a flow diagram of an exemplary method for controllingonline advertising campaigns, consistent with embodiments of the presentdisclosure; and

FIG. 10 illustrates a graphical user interface (GUI) for controllingonline advertising campaigns, consistent with embodiments of the presentdisclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to exemplary embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

FIG. 1 illustrates an exemplary system 100 for controlling onlineadvertising campaigns, consistent with embodiments of the presentdisclosure. As shown in FIG. 1, system 100 may include one or moreadvertisers 102, publishers 104, ad servers 106, and controllers 108,that are in communication with one another through a network, such asthe Internet 110. The number and orientation of the computing componentsin FIG. 1 is provided for purposes of illustration only. Any othernumber and orientation of components is possible. For example, one ormore of advertisers 101, publishers 104, ad servers 106, and controllers108 may be combined or co-located and/or communicate directly with oneanother, instead of over Internet 110. The components of FIG. 1 mayinclude any type or configuration of computers and/or servers, such as,for example, a server cluster, a server farm, load balancing servers,distributed servers, etc. In addition, each component may include one ormore processors, memories or other data storage devices (i.e.,computer-readable storage media), such as hard drives, NOR or NAND flashmemory devices, or Read Only Memory (ROM) devices, etc., communicationsdevices, and/or other types of computing elements.

Advertisers 102 may include entities having online advertisements (e.g.,banner ads, pop-ups, etc.) they desire to deliver to online consumers.Advertisers 102 may interact with publishers 104, ad servers 106, and/orcontrollers 108 through computers or servers connected to the Internet110. Thus, advertisers 102 may be able to communicate advertisinginformation, such as ad information, targeting information, consumerinformation, budget information, bidding information, etc., to otherentities in system 100.

Publishers 104 may include entities having inventories of availableonline advertising space. For example, publishers 104 may include onlinecontent providers, search engines, e-mail programs, web-basedapplications, or any entity or program having online user traffic.Publishers 104 may interact with advertisers 102, ad servers 106, and/orcontrollers 108 via computers or servers connected to the Internet 110.Thus, publishers 104 may be able to communicate inventory information,such as site information, demographic information, cost information,etc., to other entities in system 100.

Ad servers 106 may include servers or clusters of servers configured toprocess advertising information from advertisers 102 and/or inventoryinformation from publishers 104, either directly or indirectly. Incertain embodiments, ad servers 106 may be remote web servers thatreceive advertising information from advertisers 102 and serve ads to beplaced by publishers 104. Ad servers 106 may be configured to serve adsacross various domains of publishers 104, for example, based onadvertising information provided by advertisers 102. Ad servers 106 mayalso be configured to serve ads based on contextual targeting of websites, search results, and/or user profile information. In someembodiments, ad servers 106 may be configured to serve ads based oncontrol signals generated by controllers 108.

Controllers 108 may include computing systems configured to receiveinformation from entities in system 100, process the information, andgenerate advertising control signals to be sent to entities in system100, according to the exemplary methods described herein. Controllers108 may include any type or combination of computing systems, such asclustered computing machines and/or servers. In one embodiment, eachcontroller 108 may be an assembly of hardware, including a memory 112, acentral processing unit (“CPU”), and/or a user interface 116. Memory 112may include any type of RAM or ROM embodied in a physical,computer-readable storage medium, such as magnetic storage includingfloppy disk, hard disk, or magnetic tape; semiconductor storage such assolid state disk (SSD) or flash memory; optical disc storage; ormagneto-optical disc storage. CPU 114 may include one or more processorsfor processing data according to instructions stored in the memory, forexample to perform the disclosed methods and processes. The functions ofthe processor may be provided by a single dedicated processor or by aplurality of processors. Moreover, the processor may include, withoutlimitation, digital signal processor (DSP) hardware, or any otherhardware capable of executing software. User interface 116 may includeany type or combination of input/output devices, such as a displaymonitor, graphical user interface, touch-screen or pad, keyboard, and/ormouse.

FIG. 2 illustrates an exemplary control system 200 for controlling anonline advertising campaign 202 operating in an online advertisingnetwork 204, such as an online advertising network of system 100.Advertising network 204 may include a network or collection of one ormore publishers 104, ad severs 106, controllers 108, or other componentsof system 100. Elements of advertising network 204 may operate toreceive impression requests associated with ne or more ad inventories,e.g., from publishers such as websites or other entities with aninventory of online ad space. Advertising network 204 may also groupimpression requests for various advertising campaigns, e.g., accordingto impressions to be “targeted” based on a combination of attributesdefined by the ad requests. Advertising network 204 may also accept bidson the impression requests, e.g., from one or more controllers 108, andprocess the bids to serve ads on the ad inventories associated withpublishers 104.

Of course, any number or type of advertising campaigns 202 may beoperated within advertising network 204, across various ad servers anddomains associated with the Internet. Control system 200 may beimplemented by one or more of the advertisers 102, publishers 104, adservers 106, and/or controllers 108 described in FIG. 1. For example,control system 200 may represent the interaction of one or more ofcontrollers 108 with other entities in system 100.

In one embodiment, control system 200 may include, among other controlmodules, campaign objective controller 206, which may be at leastpartially managed by a supervisory controller 208. Campaign objectivecontroller 206 and supervisory controller 208 may comprise computers orservers connected to the Internet, as described with respect to adservers 106 and controllers 108 of system 100. Alternatively, campaignobjective controller 206 and supervisory controller 208 may be softwaremodules executed by CPUs 114 of controllers 108 and/or ad servers 106.Campaign objective controller 206 and supervisory controller 208 may beembodied entirely in hardware, entirely in software, or in a combinationof hardware and software.

Supervisory controller 208 may be provided with a set of deliveryrequirements 210, which may be adjustable design parameters set by auser. For instance, the set of delivery requirements may include dailybudget goals, daily impression delivery goals, targeting instructions,eCPM (effective cost per thousand impressions) constraint, eCPC(effective cost per click) constraint, eCPA (effective cost per action)constraint, and/or spread constraints for controlling advertising acrossinventory units and/or user targets, and/or spread constraints forcontrolling advertising across inventory units, etc. The set of deliveryrequirements 210 may be implemented by one or more controllers of system200, including campaign objective controller 206 and supervisorycontroller 208.

In one embodiment, campaign objective controller 206 may be a controllerconfigured to assist an advertising campaign 202 in meeting daily budgetand impression goals. Consistent with the disclosed embodiments,campaign objective controller 206 may be configured to control bid priceand allocation signals to optimize the spending of ad campaign revenuein accordance with an objective set by a user. Such objectives mayinclude, for example, a smoothness objective in which campaign objectivecontroller 206 manages campaign delivery to achieve smoothness inrevenue spending over the course of the campaign, and/or acost-reduction objective in which campaign objective controller 206manages campaign delivery to reduce average cost per impression(“eCPM”), average cost per click (“eCPC”), and/or average cost per otheraction (“eCPA”) (e.g., conversion), over the course of the campaign. Inone embodiment, campaign objective controller 206 may implement at leastsome of the exemplary systems and methods described in U.S. patentapplication Ser. No. 11/907,587, filed on Oct. 15, 2007 (now U.S. Pat.No. 7,835,937), the entire disclosure of which is incorporated herein byreference.

FIG. 3 depicts a flow diagram illustrating an exemplary control method300 performed by campaign objective controller 206 to control addelivery across various inventory units of an ad campaign, consistentwith embodiments of the present disclosure. As will be described in moredetail below, control method 300 may include one or more steps foradjusting the distribution of ad delivery as measured by impressions,clicks, conversions, or any other events occurring for each inventoryunit of an ad campaign.

As illustrated in FIG. 3, control method 300 may begin by detecting addelivery at a particular inventory unit (step 302). For example, themethod may involve determining how many impressions, clicks, or eventsare associated with a particular website in a particular period of time.Control method 300 may then compare ad delivery at that inventory unitto a reference value received for that inventory unit (step 304). Thereference value may be a particular quantity of ad impressions, clicks,or events that have been calculated by spread controller 208 and/orsupervisory controller 210.

The method may further involve generating updated ad control settingsfor the inventory unit based on the comparison to the reference value(step 306). The updated ad control settings may include an updated bidprice control signal and/or an updated bid allocation control signal.For example, if delivery to the particular inventory unit is higher thanthe reference value, then the bid price and/or bid allocation values maybe reduced accordingly to limit the competitiveness of the ad campaignrelative to the inventory unit.

The method may further include adjusting ad delivery to the inventoryunit by implementing the generated updated ad control settings (step308). For example, the updated bid price control signal and/or bidallocation control signal may be sent to an actuator associated with theinventory unit. The method may continue to operate in a closed-loop, byagain detecting ad delivery at the inventory unit (step 302).

FIG. 4 depicts an exemplary embodiment of campaign objective controller206, consistent with embodiments of the present disclosure. As shown inFIG. 4, campaign objective controller 206 may include a master campaigncontroller 220, a plurality of unit controllers 224-228, and a sensor238. Each campaign objective controller 206 may be associated with aparticular ad campaign 230, for which ads are served on a plurality ofinventory units 232-236. In general, campaign 230 may represent thesystem (or “plant,” as it is referred to in control theory), for whichad delivery may be controlled by campaign objective controller 206.

Master controller 220 of campaign objective controller 206 may beprovided with campaign configuration information 222. For example, asdescribed above with respect to the suite of delivery requirements 212associated with supervisory controller 208, campaign configurationinformation 222 may be defined by user input, which may include maximumbid price, maximum and minimum delivery ratios, daily budgets, targetinginformation, event requirements, and/or other campaign configurationinformation. For example, in one embodiment, campaign configurationinformation 222 may include instructions to bid no more than a maximumallowed cost for impression r_(ij) ^(max)(k) for a given inventory unit(e.g., a set of inventory cells {i,j}), set by the user (i.e.,advertiser). Campaign configuration information 222 may also includeinstructions regarding which campaign delivery objective (e.g.,smoothness, eCPM, eCPC, eCPA) the user has set for the campaign 230. Inaddition, campaign configuration information 222 may include otherconfiguration information for the campaign 230. As described above, eachinventory unit may be a single URL, an entire domain, any desired groupsof websites, audience segment, website-audience segment, etc. Thus, theuser may selectively limit delivery across inventory units of websitesor groups of websites, as desired. Master controller 220 may beconfigured to receive feedback from sensor 238 and generate deliveryrevenue reference signals r^(ref)(k) designed for particular unitcontrollers 224-228, based on the campaign configuration information 222and on the feedback from sensor 238.

Master controller 220 may be disposed in communication with theplurality of unit controllers 224-228. Unit controllers 224-228 may beindividualized controllers configured to generate updated campaign-levelcontrol signals, u^(rs)(k), designed for respective inventory units232-236 of campaign 230. For example, as shown in FIG. 4, unitcontroller 224 may generate a campaign level control signal ul^(rs)(k)for inventory unit 232 of campaign 230, unit controller 226 may generatea control signal u2 ^(rs)(k) for inventory unit 234 of campaign 230, andso on. Each control signal u^(rs)(k) may embody ad deliveryinstructions, such as instructions to modify (i.e., increase ordecrease) bid price value and/or bid allocation for each respectiveinventory unit 232-236 of campaign 230.

Master controller 220 may also be disposed in communication with sensor238, which is disposed in communication with, and configured to detectconditions of, inventory units of campaign 230. Sensor 238 may beconfigured to obtain real-time data about one or more inventory units onwhich ads are delivered for campaign 230. For example, sensor 238 maymeasure operating conditions of campaign 230, such as impression, click,event, action volumes and/or rates, and/or revenue for each inventoryunit 232-236. In one embodiment, sensor 238 may output a marginalrevenue reported at time k r^(uploaded)(k) for campaign 230.Additionally, or alternatively, sensor 238 may output metricsrepresenting other operating conditions of campaign 230, such asmarginal impression volume n(k), a reference impression volumen^(ref)(k), a preceding control signal u(k−1), and/or an uploadedimpression volume n^(uploaded)(k), for each time k of a campaign. Sensor238 may be configured to feed such data to master controller 220 and toone or more of the unit controllers 224-228.

Unit controllers 224-228 may be configured to receive reference signalsfrom master controller 220 and feedback signals from sensor 238. Unitcontrollers may further be configured to generate campaign level controlsignals, u^(rs)(k), based on those inputs for each time k of a campaign.For example, unit controllers 224-228 may be configured to modify addelivery settings (e.g., bid price and/or bid allocation) for particularinventory units of campaign 230, by sending updated ad delivery controlsignals u^(rs)(k). In general, each unit controller 224-228 may acompare a delivery reference (e.g., desired marginal revenue) signalr^(ref)(k) received at time k from master controller 220 with feedbacksignals r^(uploaded)(k) received from sensor 238, to determine how toadjust ad delivery control settings.

FIGS. 5A and 5B illustrate an exemplary embodiment for how unitcontrollers 224-226 may modify campaign level control signals sent toparticular inventory units (e.g., a set of inventory cells {i,j}) of anad campaign. Specifically, FIG. 5A illustrates a unit controllerapplying a multiplicative bid adjustment to a campaign level baselinebid allocation control signal for an inventory unit, in order togenerate an updated campaign level inventory unit bid allocation controlsignal. For example, if delivery at an inventory unit is too high, theunit controller may reduce the bid allocation value from 1.0 to 0.8 bymultiplying the bid allocation value by 0.8. Thus, the unit controllermay reduce the ad delivery to a particular inventory unit by selectivelyreducing the campaign level bid allocation control signal for thatinventory unit.

In addition to adjusting the bid allocation value, each unit controllermay also, or alternatively, adjust the bid price value for the inventoryunit. FIG. 5B illustrates a unit controller applying a multiplicativebid adjustment to a campaign level baseline bid price control signal foran inventory unit, in order to generate an updated campaign levelinventory unit bid price control signal. For example, if delivery at aninventory unit is too high, the unit controller may modify the campaignlevel control signal to reduce the bid price value 0.8 to 0.6. Thus, theunit controller may reduce the ad delivery to a particular inventoryunit by selectively reducing the competitiveness of the bid price forthat inventory unit. The ad delivery to a particular inventory unit of acampaign may be selectively reduced by any desired combination or ratioof reductions in the bid price and bid allocation campaign level controlsignals. Of course, the unit controllers may increase or decrease thebid allocation and/or bid price by any other suitable mathematicaloperation, such as addition, or the application of any suitablefunction. Although FIGS. 5A and 5B illustrate separate unit controllersfor bid allocation and bid price, it is noted that the same common unitcontroller may perform both functions, depending upon the desiredimplementation.

FIG. 6 illustrates one exemplary embodiment for one of the unitcontrollers 224-228 associated with an inventory unit 232-236,consistent with embodiments of the present disclosure. In general, foreach unit time k of a campaign, a unit controller may receive a signalrepresenting marginal revenue, r^(uploaded)(k), consumed at theinventory unit (i.e., “feedback”), and the delivery revenue referencesignal, r^(ref)(k), from the master controller 220. The marginalrevenue, r^(uploaded)(k), may represent the amount of marginal revenueconsumed by the inventory unit since the last sampling, as detected by asensor at the inventory unit. The delivery reference revenue signal,r^(ref)(k), may be a desired revenue of advertising events (e.g.,impressions, clicks, conversions, etc.) consumed by an inventory unit,as generated by master controller 220. Using the marginal revenue,r^(uploaded)(k), and delivery revenue reference signal, r^(ref)(k), theunit controller may be configured to generate a campaign level controlsignal u^(rs)(k) to be applied to an inventory unit 232-236 of acampaign. As described above with respect to FIG. 4, the exemplarysystems and methods disclosed herein may include or utilize a pluralityof unit controllers 224-228, each being configured to generate a controlsignal u^(rs)(k) for a particular inventory unit of an ad campaign.

In one embodiment, each unit controller 224-228 may include a pluralityof different modules which, together, may be configured to calculate thecampaign level control signal u^(rs)(k) based on the two input signals,r^(uploaded)(k), r^(ref)(k), and historical data of the modules and theinventory unit. For example, in one embodiment, each unit controller mayinclude one or more of the following modules: a day-of-week compensator240, a moving average filter 242, a marginal revenue compensator 244, aplant gain estimator 246, a normalizer 248, and a core feedbackcontroller 250.

Day-of-week compensator 240 may be configured to account for the factthat website traffic is generally lighter (i.e., less volume) for mostwebsites on the weekends, as opposed to weekdays. Day-of-weekcompensator 240 may be configured to allow advertising event volume tofollow its natural day-of-week pattern, such that a website's campaignlevel control signal, u^(rs)(k), remains relatively constant throughoutthe week. In one embodiment, day-of-week compensator 240 may achievethis goal by compensating the marginal uploaded revenue r^(uploaded)(k)consumed by the inventory unit, based on a ratio between the averageweekday volume and the average weekend volume, which may be denoted as,p^(weekday2weekend).

Thus, in one embodiment, the input to day-of-week compensator 240 may bethe marginal revenue r^(uploaded)(k) where k is a timestamp of thecampaign, and the output may be a compensated value of the marginaluploaded revenue r^(uploaded,c)(k), which has been computed as follows:

${r^{{uploaded},c}(k)} = \left\{ \begin{matrix}{{\frac{{h^{weekdays}p^{{weekday}\; 2{weekend}}} + \left( {168 - h^{weekdays}} \right)}{168p^{weekday2weekend}}{r^{uploaded}(k)}},} & {{if}\mspace{14mu} k\mspace{14mu} {in}\mspace{14mu} {weekdays}} \\{\frac{{h^{weekdays}p^{{weekday}\; 2{weekend}}} + \left( {168 - h^{weekdays}} \right)}{168}{r^{uploaded}(k)}} & {otherwise}\end{matrix} \right.$

where h^(weekdays) represent the number of weekday hours in one week,(e.g., 120 hours=5 weekdays*24 hours/day), and 168 represents the numberof hours in one week. The ratio of p^(weekday2weekend) may be selectedby a user based on any known experimental data, based on, for example,the corresponding ratio of the whole network or a ratio specific to acampaign at an inventory unit. Thus, day-of-week compensator 240 maygenerate a compensated value of the marginal uploaded volumer^(uploaded,c)(k).

Moving average filter 242 may receive the compensated value of themarginal uploaded revenue, r^(uploaded)(k), from day-of-week compensator240. Similar to day-of-week compensator 240, moving average filter 242may be configured to average out a time-of-day pattern inherent in thenetwork. Moving average filter 242 may also be configured to reduce anynoise in the volume signal. For this purpose, moving average filter 242may incorporate any desired time interval, such as an interval of 24hours (i.e., a “lookback window”). During a particular 24 hour lookbackwindow, moving average filter 242 may generate a value,r^(uploaded,24hrMA)(k), which may represent the 24-hour moving sum ofr^(uploaded,c)(k) for a given lookback window. Of course,r^(uploaded,24hrMA)(k) may represent the moving sum ofr^(uploaded,24hrMA)(k) for any predetermined time interval. In anembodiment in which lookback window is 24 hours, moving average filter242 may be implemented as follows:

${{r^{{uploaded},{24{hrMA}}}(k)} = {\frac{1}{24f^{sampling}}{\sum\limits_{l = 0}^{l = {{24f^{sampling}} - 1}}\; {r^{{uploaded},{2c}}\left( {k - l} \right)}}}},$

where f^(sampling) is the sampling frequency in cycles/hour, andr^(uploaded,2c)(k) represents the marginal revenue after hard-stop-cap(DOW) compensation.

Marginal revenue compensator 244 may be configured to compensate for themarginal uploaded value of the moving sum, r^(uploaded,24hrMA)(k), inthe event that a campaign is paused for external reasons. For example,if a campaign is paused due to daily budgetary or delivery constraintsimposed by another controller, the marginal uploaded revenue, themarginal revenue r^(uploaded)(k), and therefore the compensated marginaluploaded revenue, r^(uploaded,c)(k), may both reduce to zero. In suchcircumstances, unless compensation is performed, the controller mayoperate in an open-loop mode in which the campaign level control signal,u^(rs)(k), is aggressively increased out of a desire to increase eventvolume. In order to compensate for such low volume events, marginalrevenue compensator 244 may be configured to receive the marginaluploaded value of the moving sum, r^(uploaded,24hrMA)(k) from movingaverage filter 242.

Marginal revenue compensator 244 may also receive the delivery revenuereference, r^(ref)(k), from master controller 220, the moving sum,r^(uploaded,24hrMA)(k) compensated by day-of-week compensator 240, frommoving average filter 242, a binary signal, u^(HSC)(k), which indicateswhether the campaign is active. Marginal volume compensator 244 may beconfigured to generate a volume output, denoted r^(uploaded,2c)(k), ascalculated below:

${r^{{uploaded},{2c}}(k)} = \left\{ \begin{matrix}{{{r^{{uploaded},c}(k)} + {\alpha^{MVC}\frac{24f^{sampling}e^{{\hat{\beta}}_{1}{\sin {({{2\pi \; {k/24}} + {\hat{\phi}}_{1}})}}}}{\sum_{l = 0}^{l = {{24f^{sampling}} - 1}}e^{{\hat{\beta}}_{1}{\sin {({{2\pi \; {l/24}} + {\hat{\phi}}_{1}})}}}}{r^{ref}(k)}}},} & {{{if}\mspace{14mu} {u^{HSC}\left( {k - 1} \right)}} = 1} \\{{r^{{uploaded},c}(k)},} & {otherwise}\end{matrix} \right.$

where α^(MVC) is a design parameter, defaulted to be 1.

Thus, marginal revenue compensator 244 may be configured to modify themarginal uploaded revenue (i.e., increase it by a multiple of thereference value r^(ref)(k)), in the event that the campaign is notactive and u^(HSC)(k) is set to 1. Otherwise, if the campaign is activeand u^(HSC)(k) is set to 0, then marginal uploaded volumer^(uploaded,2c)(k) may be unaltered by marginal volume compensator 244.

Plant gain estimator 246 may be configured to calculate the estimatedplant gain, ǵ(k), based on the compensated moving average of themarginal uploaded revenue, r^(uploaded,2c) and the control signalgenerated in the previous sampling period, u^(rs)(k−1). Thus, plant gainestimator 246 may receive the compensated moving average of the marginaluploaded revenue, r^(uploaded,2c)(k), from the marginal volumecompensator 244. In one embodiment, plant gain estimator 246 may definethe gain, ǵ(k), as follows:

${{\hat{g}(k)} = {{\lambda^{gain}{\hat{g}\left( {k - 1} \right)}} + {\left( {1 - \lambda^{gain}} \right)\frac{n^{{uploaded},{24{hrMA}}}(k)}{u^{rs}\left( {k - 1} \right)}}}},$

where λ^(gain) is a design parameter, defaulted to be 0.05. Thus, plantgain estimator 246 may generate a gain signal, ǵ(k), which is a functionof the control signal of the previous sampling period, u^(rs)(k−1), andthe compensated moving average of the marginal uploaded revenue,r^(uploaded,2c)k) .

Normalizer 248 may receive the gain signal, ǵ(k), from plant gainestimator 246, the daily volume reference r^(ref)(k) from the masterspread controller 220, and the compensated moving average of themarginal uploaded revenue, r^(uploaded,2c)(k). The normalizer 248 maycalculate a normalized reference r ^(24hrMA)(k) and a normalizedfeedback signal r ^(MA)(k) based on the revenue reference r^(ref)(k),the compensated moving-average of the marginal uploaded revenuer^(uploaded,2c)(k) , the estimated plant gain ǵ(k), and the controlsignal generated in the previous sampling period u^(rs)(k−1). In oneembodiment, normalizer 248 may calculate the normalized feedback signalr ^(24hrMA)(k) as follows:

${{\overset{\_}{r}}^{24{hrMA}}(k)} = \left\{ {\begin{matrix}{{{\lambda^{{normalization}_{\overset{\_}{r}}24{hrMA}}\left( {k - 1} \right)} + {\left( {1 - \lambda^{normalization}} \right){u^{rs}\left( {k - 1} \right)}}},} & {{{if}\mspace{14mu} {\hat{g}(k)}} = 0} \\{{{\lambda^{{normalization}_{\overset{\_}{r}}24{hrMA}}\left( {k - 1} \right)} + {\left( {1 - \lambda^{normalization}} \right)\frac{r^{24{hrMA}}(k)}{\hat{g}(k)}}},} & {otherwise}\end{matrix},} \right.$

where λ^(normalization) is a design parameter with a default values0.975. In one embodiment, normalizer 248 may calculate the normalizedreference r ^(ref)(k) _(as follows:)

${{{\overset{\_}{r}}^{ref}(k)} = {{\lambda^{{normalization}_{\overset{\_}{r}}{ref}}\left( {k - 1} \right)} + {\left( {1 - \lambda^{normalization}} \right)\min \left\{ {\frac{r^{{ref},{flt}}(k)}{\hat{}(k)},{\alpha^{{ref}\; 2{feedback}_{\overset{\_}{r}}24{hrMA}}(k)}} \right\}}}},$

where α^(ref2feedback) is a design parameter with default value 1, and

r ^(ref,flt)(k)=λ^(ref) r ^(ref,flt)(k−1)+(1−λ^(ref))r ^(ref)(k),

where λ^(ref) is a design parameter with default value 0.975. Normalizer248 may be initialized with the following values of r ^(24hrMA)(k),r^(ref,flt)(k), and r ^(ref)(k):

of r ^(24hrMA)(0)=u ^(rs)(0)

r ^(ref,flt)(0)=n ^(ref)(0)

r ^(ref)(0)=u ^(rs)(0)

Thus, normalizer 248 may be configured to generate a normalized marginalreference r ^(ref)(k) (i.e., “reference signal”) and a normalized movingaverage uploaded volume r ^(24hrMA)(k) (i.e., “feedback signal”) to besent to core feedback controller 250 for generation of an updated addelivery campaign level control signal, u^(rs)(k).

Core feedback controller 250 may be configured to generate the updatedcontrol signal u^(rs)(k))based on the normalized marginal reference r^(ref)(k) (i.e., “reference signal”) and the normalized moving averageuploaded volume r ^(24hrMA)(k) (i.e., the “feedback signal”), accordingto the following:

u ^(rs)(k)=max{u ^(rs,min), min{u ^(rs,max) , v ^(rs)(k)}}

where u^(rs,min) is the minimum allowed campaign level control signal,u^(rs,max) is the maximum allowed campaign level control signal, andv^(rs)(k) is defined as:

v ^(rs)(k)=(1+r ₁)u ^(rs)(k−1)−r ₁ u ^(rs)(k−2)−s ₀ r^(uploaded,24hrMA)(k)+(s ₀ +t ₀) r ^(ref)(k)−t ₀(1+r ₀) r ^(ref)(k−1)+t₀r_(1il r) ^(ref)(k−2)

The core feedback controller 250 may be initialized with the following:

u ^(rs)(l)=u ^(rs)(0)

r ^(ref)(l)= r ^(ref)(1)

for all l<0 , where u^(rs)(0) is the initial campaign level controlsignal and is set based on campaign delivery requirements.

Thus, in general, core feedback controller 250 may generate a campaignlevel control signal, u^(rs)(k), which is designed for a particularinventory unit, based on the previous control signal implemented by theinventory unit, u^(rs)(k−1), the uploaded advertising event revenue,r^(uploaded)(k), and the reference revenue signal, r^(ref)(k). Asdescribed above, the uploaded advertising event revenue,r^(uploaded)(k), implemented by core feedback controller 250 may havebeen compensated, normalized, and/or otherwise adjusted by one or moreof the day-of-week compensator 240, moving average filter 242, marginalrevenue compensator 244, plant gain estimator 246, and normalizer 248,to generate a normalized moving average uploaded revenue r ^(24hrMA)(k).The reference revenue signal, r^(ref)(k), received from mastercontroller 220, may have been normalized by normalizer 248, to generatea normalized marginal reference r ^(ref)(k).

In one embodiment, the campaign level control signal, u^(rs)(k),generated by core feedback controller 250 may be designed to adjust oroptimize a future uploaded advertising event volume, r^(uploaded)(k),closer to the reference revenue signal r^(ref)(k) (i.e., performclosed-loop feedback). As described above with respect to FIGS. 5A and5B, control signal, u^(rs)(k), may be a vector or matrix comprising avariety of control instructions, such as a campaign level bid pricecontrol signal, a campaign level bid allocation control signal, and/orany other targeting or budgetary instructions that impact futureadvertising event volume for the inventory unit. Thus, each unitcontroller 224-228 associated with an inventory unit of an ad campaignmay comprise, among other things, a core feedback controller 250configured to adjust or update the bid price and bid allocationcomponents of a campaign level control signal u^(rs)(k) such that theadvertising event revenue, r^(uploaded)(k), generally converges on thereference volume signal, r^(ref)(k), if possible. In some circumstances,it may be difficult or impossible for advertising event volume,r^(uploaded)(k), to completely converge on the reference volume signal,r^(ref)(k), as desired, due to strong time-of-day patterns, or forexample, if the control signals have become saturated (i.e., furtheradjustment of the bid allocation and/or bid price can no longer affectthe event volume). Accordingly, it is noted that the term “optimize,” asused herein, does not necessarily mean to achieve the best possibleresult under a given set of circumstances, but rather to control or tendtoward a desired result.

FIG. 6 also illustrates that each inventory unit 232-236 may include orotherwise be associated with a unit actuator 252 that provides actuationsignals, at the inventory unit level, to a bidding market 254. In oneembodiment, unit actuator 252 may be part of control system 200 andimplemented at a controller 108 described in FIG. 1. For example, unitactuator 252 may be part of campaign objective controller 206 (FIG. 2).But in other configurations, unit actuator 252 may be implemented at oneor more of one or more of the advertisers 102, publishers 104, adservers 106, and/or controllers 108. As with other elements of system100, unit actuator 252 may comprise an assembly of computing hardwareand/or software components configured to performed the disclosedprocesses.

FIG. 7 illustrates unit actuator 252 in an inventory unit actuationenvironment 400, consistent with embodiments of the present disclosure.As shown in FIG. 7, unit actuator 252 may use campaign configurationinformation 222. In one embodiment, the campaign configurationinformation 222 may indicate one of the following optimizationobjectives, discussed above, selected by a user: smoothness, reducedaverage cost per impression (“eCPM”), reduced average cost per click(“eCPC”), and/or reduced average cost per conversion (“eCPA”). Othertypes of optimization objectives are contemplated, however.Additionally, the campaign configuration information 222 may furtherindicate the maximum allowed cost for impression, r_(ij) ^(max)(k), forthe inventory unit 232-236 (e.g., cell {i,j}), also set by the user.(Exemplary techniques by which the user may select or set the campaignconfiguration information 222 are discussed in greater detail below.)Also, as shown in FIG. 7, unit actuator 252 may receive the campaignlevel control signal, u^(rs)(k), as generated and output by feedbackcontroller 250 (FIG. 6), as well as performance feedback informationassociated with the particular inventory unit 232-236. In oneembodiment, the performance feedback information may include theperformance information collected by the sensor 238 for the particularinventory unit 232-236, such as rate and/or volume of impressions,click-through, conversions, actions, or other types of ad events.

Unit actuator 252 may be configured to calculate a final bid priceb_(ij) ^(final)(k) and a final bid allocation a_(ij) ^(final)(k) for theinventory unit 232-236 based on one or more factors. Specifically, unitactuator 252 may calculate the final bid price b_(ij) ^(final)(k) andfinal bid allocation a_(ij) ^(final)(k) for the selected campaignobjective based on one or more of the maximum allowed cost forimpression, r_(ij) ^(max)(k) , the campaign level control signal,u^(rs)(k), or the performance feedback information for the inventoryunit 232-236 received from the sensor 238. Additionally, unit actuator252 may be configured to generate signals conveying the calculated finalbid price b_(ij) ^(final)(k) and final bid allocation a_(ij)^(final)(k), and may provide these signals to market 254 to bid onimpressions from inventory unit 232-236.

FIG. 8 depicts a flow diagram illustrating an exemplary method 500 thatmay be implemented by actuator 252 for calculating a final bid priceb_(ij) ^(final)(k) and final bid allocation a_(ij) ^(final)(k) for aparticular inventory unit 232-236. In one embodiment, method 500 may beperformed by one or more processors associated with actuator 252, suchas one or more processors of a controller 108.

Method 500 may include receiving a campaign optimization objective setby a user for the campaign (step 502). For example, as discussed below,a user associated with one of advertisers 102 may use a graphical userinterface (GUI), such as a web portal to one of controllers 108, toinput campaign configuration information 222 for a desired ad campaign.The input campaign configuration information 222 may include, amongother things, a desired campaign optimization objective. For example,the user may select one of: a smoothness objective to smoothen revenuespending evenly over the course of the campaign, an eCPM campaign toreduce average cost per impression over the course of the campaign, aeCPC campaign to receive the average cost per click over the course ofthe campaign, or an eCPA campaign to reduce the average cost perconversion over the course of the campaign. Other types of campaignoptimization objectives are contemplated and within the scope of thedisclosed embodiments.

Method 500 may further include receiving a maximum bid price, r_(ij)^(max)(k), for impression, click, conversion, action, or other ad eventfor the inventory unit 232-236, also set by the user (step 405). Themaximum bid price, r_(ij) ^(max)(k), may represent the maximum dollaramount the user is willing to pay to win a bid for an impression fromthe inventory unit 232-236. As with step 502, the user may provide themaximum bid price, r_(ij) ^(max)(k)when inputting the campaignconfiguration information 222 via the GUI and/or web portal associatedwith one of controllers 108.

Method 500 may further include receiving performance feedbackinformation for the inventory unit 232-236 (step 506). For example,sensor 238 may monitor market 254 to collect and/or measure, among otherinformation, the rate and/or volume of impressions, click-through,conversions, actions, or other ad events associated with the inventoryunit 232-236. That is, in step 506, method 500 may determine how theinventory unit 232-236 is performing in the market 254 in terms ofInternet consumers' responses to ad content provided at the inventoryunit 232-236. This information may be provided to one of controllers 108in connection with performing method 500. Step 506 may occur inreal-time or periodically, e.g., at a desired sampling frequencyf^(sampling)(k) for the campaign.

Method 500 may further include receiving the campaign level controlsignal u^(rs)(k) for the inventory unit 232-236 (step 508). For example,as shown in FIG. 6, actuator 252 may receive the campaign level controlsignal u^(rs)(k) as input from core feedback controller 250. As withstep 506, step 508 may also occur in real-time or periodically, e.g., atthe desired sampling frequency f^(sampling)(k) for the campaign.

Method 500 may further include calculating a final bid price b_(ij)^(final)(k) and a final bid allocation a_(ij) ^(final)(k) for theinventory unit 232-236 based on the selected campaign objective, themaximum allowed cost for impression, r_(ij) ^(max)(k), the campaignlevel control signal, u^(rs)(k), and the performance feedbackinformation received from the sensor 238 (step 510). As with otheroperations in method 500, step 510 may also occur in real-time orperiodically, e.g., at the desired sampling frequency f^(sampling)(k)for the campaign. Finally, method 500 may include sending the calculatedfinal bid price b_(ij) ^(final)(k) and final bid allocation a_(ij)^(final)(k) to market 512 (step 512). In particular, when an impressionfrom the inventory unit 232-236 becomes available (i.e., up forauction), unit actuator 252 may submit a bid on that impression based onthe calculated final bid price b_(ij) ^(final)(k) and final bidallocation b_(ij) ^(final)(k).

FIG. 9 depicts a flow diagram illustrating an exemplary representationof step 510 (FIG. 8), i.e., the step for calculating a final bid priceb_(ij) ^(final)(k) and final bid allocation a_(ij) ^(final)(k), ingreater detail. In one embodiment, in connection with step 510, method500 may determine whether the selected campaign optimization objectiveis the smoothness, eCPM, eCPC, or eCPA objective (step 600). That is,method 500 may determine which campaign optimization objective the userhas selected via the GUI or web portal associated with controller 108.

If the selected campaign objective is smoothness, method 500 may includecalculating a final bid price b_(ij) ^(final)(k) and final bidallocation a_(ij) ^(final)(k) specific to the smoothness objective (step602). In one embodiment, unit actuator 252 may calculate the final bidprice b_(ij) ^(final)(k) and final bid allocation a_(ij) ^(final)(k) forthe smoothness objective according to the following algorithm:

${b_{ij}^{final}(k)} = \frac{\frac{{u^{p,{campaign}}(k)}r_{ij}^{\max}}{1 + m^{Agency}} - r^{idf} - r^{edf}}{1 + m^{Aol}}$a_(ij)^(final)(k) = a_(ij)^(baseline)(k)u^(a, campaign)(k),

where r^(idf) and r^(edf) are internal and external data fees, chargedper impression, related to the cost of serving ads and verifying data;m^(Agency) is an agency commission fee per impression, in the form of apercentage of the maximum bid price r_(ij) ^(max)(k) for an impression,charged by the ad agency; m^(Aol) is a management fee per impression, inthe form of a percentage of the final bid price b_(ij) ^(final)(k), paidby the user for using the disclosed campaign management services; anda_(ij) ^(baseline)(k) is a preset baseline allocation for the inventoryunit 232-236 (e.g., cell {i,j}). Additionally, u^(p,campaign)(k)represents a value of a campaign level price control signal generated attime k, while u^(a,campaign)(k) represents a value of a campaign levelbid allocation control signal generated at time k. In connection withthe smoothness optimization objective, u^(p,campaign)(k) andu^(a,campaign)(k) are set as follows:

u ^(p,campaign)(k)=1, u ^(a,campaign)(k)=u ^(rs)(k)

Accordingly, based on the algorithm above, it is to be appreciated thatwhen using the smoothness objective, the final bid price b_(ij)^(final)(k) is generally proportional to the maximum bid price r_(ij)^(final)(k) for an impression which, as discussed, is set in advance bythe user when configuring the campaign. Additionally, the value of thefinal bid allocation a_(ij) ^(final)(k) is modified by the campaignlevel control signal u^(rs)(k) to control the pace of the advertisingcampaign. Thus, in comparison with conventional smoothness advertisingcampaign control, the disclosed smoothness objective algorithm mayreduce the average cost per impression paid by the user (e.g.,advertiser) because the user has the ability to set the maximum bidprice r_(ij) ^(max)(k) for an impression at the time of setting up thecampaign.

Returning to FIG. 9, if the selected campaign objective is eCPM, method500 may include calculating a final bid price b_(ij) ^(final)(k) andfinal bid allocation a_(ij) ^(final)(k) for the eCPM objective (step604). In one embodiment, unit actuator 252 may calculate the final bidprice b_(ij) ^(final)(k) and final bid allocation a_(ij) ^(final)(k) forthe eCPM objective according to the following algorithm:

${b_{ij}^{final}(k)} = \frac{\frac{{u^{p,{campaign}}(k)}r_{ij}^{\max}}{1 + m^{Agency}} - r^{idf} - r^{edf}}{1 + m^{Aol}}$a_(ij)^(final)(k) = a_(ij)^(baseline)(k)u^(a, campaign)(k),

where, again, r^(idf) and r^(edf) are internal and external data fees,charged per impression, related to the cost of serving ads and verifyingdata; m^(Agency) is an agency commission fee per impression, in the formof a percentage of the maximum bid price r_(ij) ^(max)(k) for animpression, charged by the ad agency; m^(Aol) is a management fee perimpression, in the form of a percentage of the final bid price b_(ij)^(final)(k), paid by the user for using the disclosed campaignmanagement services; and a_(ij) ^(baseline)(k) is a baseline allocationfor the inventory unit 232-236 (e.g., cell {i,j}). As with thesmoothness objective, u^(p,campaign)(k) represents a value of a campaignlevel price control signal generated at time k, while u^(a,campaign)(k)represents a value of a campaign level bid allocation control signalgenerated at time k. But, in contrast with the smoothness objective,u^(p,campaign)(k) and u^(a,campaign)(k) in the eCPM objective are set asfollows:

u ^(p,campaign)(k)=u ^(rs)(k), u ^(a,campaign)(k)=1

Accordingly, based on the algorithm above, it is to be appreciated thatwhen using the eCPM objective, the final bid price b_(ij) ^(final)(k)generally proportional to the maximum bid price r_(ij) ^(max)(k) for animpression, as modified by the campaign level control signal u^(rs)(k).Additionally, the final bid allocation a_(ij) ^(final)(k) is essentiallyset equal to the baseline allocation a_(ij) ^(baseline)(k) for theinventory unit 232-236, meaning that controller 108 may control thecampaign as to participate in the baseline percentage a_(ij)^(baseline)(k) of auctions for the inventory unit 232-236. Accordingly,in the eCPM objective, campaign pacing may be controlled generally basedon the final bid price b_(ij) ^(final)(k) modified by the campaign levelcontrol signal u^(rs)(k). Thus, in comparison with conventionaladvertising campaign control, the disclosed eCPM optimization objectivemay reduce the average cost per impression paid by the user (e.g.,advertiser) because the user has the ability to set the maximum bidprice r_(ij) ^(max)(k) for an impression at the time of setting up thecampaign.

If the selected campaign objective is eCPC, method 500 may includecalculating a final bid price b_(ij) ^(final)(k) and final bidallocation a_(ij) ^(final)(k) specific to the eCPC objective (step 606).In one embodiment, unit actuator 252 may calculate the final bid priceb_(ij) ^(final)(k) and final bid allocation a_(ij) ^(final)(k) for theeCPC objective according to the following algorithm:

${b_{ij}^{final}(k)} = \frac{\frac{\min \left\{ {{{u^{p,{campaign}}(k)}T^{cost}{{\hat{p}}_{ij}^{I\; 2C}(k)}},r_{ij}^{\max}} \right\}}{1 + m^{Agency}} - r^{idf} - r^{edf}}{1 + m^{Aol}}$a_(ij)^(final)(k) = a_(ij)^(baseline)(k)u^(a, campaign)(k),

where r^(idf) and r^(edf) are internal and external data fees, chargedper impression, related to the cost of serving ads and verifying data;m^(Agency) is an agency commission fee per impression, in the form of apercentage of the maximum bid price r_(ij) ^(max)(k) for an impression,charged by the ad agency; m^(Aol) is a management fee, in the form of apercentage of the final bid price b_(ij) ^(final)(k), paid by the userfor using the disclosed campaign management services; and a_(ij)^(baseline)(k) is a baseline allocation for the inventory unit 232-236(e.g., cell {i,j}). Further, T^(cost)(k) is a target cost-per-click forthe inventory 232-236, which may be set by the user (i.e., advertiser)when setting up the campaign. And {circumflex over (p)}_(ij) ^(12C)(k)is an estimated click-through rate for the inventory unit 232-236 (e.g.,cell {i,j}), as determined based on the performance feedback informationfrom sensor 208. For example, sensor 208 may detect clicks on adsassociated with one or more inventory units 232-236 within a period oftime, and a click-through rate may be determined or estimated based onthe detected number of clicks (e.g., by actuator 252). Similar to theother optimization objectives discussed above, u^(p,campaign)(k)represents a value of a campaign level price control signal generated attime k, while u^(a,campaign)(k) represents a value of a campaign levelbid allocation control signal generated at time k. And, as with the eCPMobjective, in the eCPC objective, u^(p,campaign)(k) andu^(a,campaign)(k) are set as follows:

u ^(p,campaign)(k)=u ^(r,s)(k), u ^(a,campaign)(k)=1

Accordingly, based on the algorithm above, it is to be appreciated thatwhen using the eCPC objective, the final bid price b_(ij) ^(final)(k) isgenerally proportional to the minimum of (1) the maximum bid pricer_(ij) ^(max)(k) for an impression (set by the user) and (2) the targetcost-per-click T^(cost)(k) for the inventory 232-236 (also set by theuser) multiplied by the estimated click-through rate {circumflex over(p)}_(ij) ^(12C)(k) for the inventory unit 232-236, as modified by thecampaign level control signal u^(rs)(k). That is, the final bid priceb_(ij) ^(final)(k) is generally proportional to whichever is lessbetween: (1) the maximum bid price r_(ij) ^(max)(k) for an impression or(2) the target cost-per-click T^(cost)(k) for the inventory 232-236multiplied by the estimated click-through rate {circumflex over(p)}_(ij) ^(12C)(k) for the inventory unit 232-236, as modified by thecampaign level control signal u^(rs)(k). Additionally, the final bidallocation a_(ij) ^(final)(k) is essentially set equal to the baselineallocation a_(ij) ^(baseline)(k) for the inventory unit 232-236, meaningthat actuator 252 may control the campaign as to participate in thebaseline percentage a_(ij) ^(baseline)(k) auctions for the inventoryunit 232-236.

Accordingly, in the eCPC objective, campaign pacing may be controlledgenerally based on whichever is less between: (1) the maximum bid pricer_(ij) ^(max)(k) for an impression or (2) the target cost-per-clickT^(cost)(k) for the inventory 232-236 multiplied by the estimatedclick-through rate for the inventory unit 232-236 {circumflex over(p)}_(ij) ^(12C)(k), as modified by the campaign level control signalu^(rs)(k). Thus, in comparison with conventional advertising campaigncontrol, which tends to maximize ad revenue for the ad network, thedisclosed eCPC algorithm can reduce the average cost-per-click paid bythe user (e.g., advertiser). This is because the algorithm takes intoconsideration (1) the maximum bid price r_(ij) ^(max)(k) for animpression that the user is willing to pay as well as (2) the “value” ofthe inventory 232-236, as represented by the target cost-per-clickT^(cost)(k) for the inventory 232-236 (set by the user) multiplied bythe estimated click-through rate for the inventory unit 232-236{circumflex over (p)}_(ij) ^(12C)(k), and generates the final bid priceb_(ij) ^(final)(k) based on the lesser of the two. Accordingly, in thedisclosed eCPC algorithm, the user may not “pay” more for an impressionthan what it is worth in terms of the performance of the inventory232-236 with respect to click-through rate {circumflex over (p)}_(ij)^(12C)(k) for ads placed therein. And, at the same time, the algorithmmay ensure that the user does not pay more that what he/she has set asthe maximum bid price r_(ij) ^(max)(k) for an impression whenconfiguring the campaign.

Finally, if the selected campaign objective is eCPA, method 500 mayinclude calculating a final bid price b_(ij) ^(final)(k) and final bidallocation a_(ij) ^(final)(k) specific to the eCPA objective (step 608).In one embodiment, unit actuator 252 may calculate the final bid priceb_(ij) ^(final)(k) and final bid allocation a_(ij) ^(final)(k) for theeCPA objective according to the following algorithm:

${b_{ij}^{final}(k)} = \frac{\frac{\min \left\{ {{{u^{p,{campaign}}(k)}T^{cost}{{\hat{p}}_{ij}^{I\; 2A}(k)}},r_{ij}^{\max}} \right\}}{1 + m^{Agency}} - r^{idf} - r^{edf}}{1 + m^{Aol}}$a_(ij)^(final)(k) = a_(ij)^(baseline)(k)u^(a, campaign)(k),

where, again, r^(idf) and r^(edf) are internal and external data fees,charged per impression, related to the cost of serving ads and verifyingdata; m^(Agency) is an agency commission fee per impression, in the formof a percentage of the maximum bid price r_(ij) ^(max)(k) (set by theuser) for an impression, charged by the ad agency; m^(Aol) is amanagement fee, in the form of a percentage of the final bid priceb_(ij) ^(final)(k), paid by the user for using the disclosed campaignmanagement services; and a_(ij) ^(baseline)(k) is a baseline allocationfor the inventory unit 232-236 (e.g., cell {i,j}). Further, T^(cost)(k)is a target cost-per-conversion for the inventory 232-236, which may beset by the user (i.e., advertiser), and {circumflex over (p)}_(ij)^(12A)(k) is an estimated conversion rate for the inventory unit 232-236(e.g., cell ({i,j}), as determined based on the performance feedbackinformation received from sensor 208. For example, sensor 208 may detectconversions of ads associated with one or more inventory units 232-236within a period of time, and a conversion rate may be determined orestimated based on the detected number of clicks (e.g., by actuator252). Similar to the other optimization objectives discussed above,u^(p,campaign)(k) represents a value of a campaign level price controlsignal generated at time k, while u^(a,campaign)(k) represents a valueof a campaign level bid allocation control signal generated at time k.And, as with the eCPC objective, in the eCPA objective,u^(p,campaign)(k) and u^(a,campaign)(k) are set as follows:

u ^(p,campaign)(k)=u ^(r,s)(k), u ^(a,campaign)(k)=1

Accordingly, based on the algorithm above, it is to be appreciated thatwhen using the eCPA objective, the final bid price b_(ij) ^(final)(k) isgenerally proportional to the minimum of (1) the maximum bid pricer_(ij) ^(max)(k) for an impression that the user is willing to pay and(2) the target cost-per-conversion T^(cost)(k) for the inventory 232-236multiplied by the estimated conversion rate {circumflex over (p)}_(ij)^(12A)(k) for the inventory unit 232-236, as modified by the campaignlevel control signal u^(rs)(k). That is, the final bid price b_(ij)^(final)(k) is generally proportional to whichever is less between: (1)the maximum bid price r_(ij) ^(max)(k) for an impression and (2) thetarget cost-per-conversion T^(cost)(k) for the inventory 232-236multiplied by the estimated conversion rate {circumflex over (p)}_(ij)^(12A)(k) or the inventory unit 232-236, as modified by the campaignlevel control signal u^(rs)(k). Additionally, the final bid allocationa_(ij) ^(final)(k) is essentially set equal to the baseline allocationa_(ij) ^(baseline)(k) for the inventory unit 232-236, meaning thatcontroller 108 may control the campaign as to participate in thebaseline percentage a_(ij) ^(baseline)(k) auctions for the inventoryunit 232-236.

Accordingly, in the disclosed eCPA objective, campaign pacing may becontrolled generally based on whichever is less between: (1) the maximumbid price r_(ij) ^(max)(k) for an impression or (2) the targetcost-per-conversion T^(cost)(k) for the inventory 232-236 multiplied bythe estimated conversion rate {circumflex over (p)}_(ij) ^(12A)(k) forthe inventory unit 232-236, as modified by the campaign level controlsignal u^(rs)(k). Thus, in comparison with conventional advertisingcampaign control, which tends to maximize ad revenue for the ad network,the disclosed eCPA optimization algorithm may reduce the averagecost-per-conversion paid by the user (e.g., advertiser). This is becausethe algorithm takes into consideration (1) the maximum bid price r_(ij)^(max)(k) for an impression the user is willing to pay as well as (2)the “value” of the inventory 232-236, as represented by the targetcost-per-conversion T^(cost)(k) (set by the user) multiplied by theestimated conversion rate {circumflex over (p)}_(ij) ^(12A)(k) for theinventory unit 232-236, and generates the final bid price b_(ij)^(final)(k) based on the lesser of the two. Accordingly, in the eCPAalgorithm, the user may not pay more for an impression than what it isworth in terms of the performance of the inventory 232-236 with respectto the conversion rate {circumflex over (p)}_(ij) ^(12C)(k) of adsplaced therein.

FIG. 10 illustrates a representation of an exemplary graphical userinterface (GUI) 700 for configuring an online advertising campaign,consistent with embodiments of the present disclosure. In oneconfiguration, GUI 700 may be a web portal or application associatedwith one or more of controllers 108 that enables a user (e.g.,associated with an advertiser 102) to configure an advertising campaignto operate as described above. As shown in the figure, GUI 700 mayinclude a campaign pacing setting 702, a daily target setting 704,campaign objective setting 706, and/or a target eCPA/eCPC setting 708.Each of these settings may comprise one or more user interface (UI)elements, such as a drop-down menu, radio button, etc., allowing theuser to select a desired value for that setting.

Campaign pacing setting 702 may allow the user to select a desiredcampaign pacing option for the advertising campaign. For example, theuser may select from one or more of: an as-fast-as-possible (AFAP)pacing option, a defined-schedule pacing option, and a keep-schedulepacing option. In one embodiment, selecting the AFAP option may causecontrol system 200 (FIG. 2) to control the advertising campaign 202 todeliver the campaign, or spend the allocated advertising budget, as fastas possible. That is, this option may cause control system 200 toattempt to win all the impressions it can without regard for a maximumpermitted bid r_(ij) ^(max)(k) for an impression and/or also withoutregard for the “value” of an inventory unit 232-236 in terms ofclick-through rate, conversion rate, or other performance metric.Accordingly, if the AFAP option is selected, none of the above-describedcampaign optimization objectives may be available to the user. Incontrast, selecting the defined-schedule pacing option may cause controlsystem 200 to attempt to smoothly spend the advertising budget such thatthe daily spending target 704 (e.g., $9,500) is spent each day. Also, ifthe keep-schedule pacing option is selected, the control system 200 mayautomatically determine a daily spending goal based on campaign deliveryhistory and market conditions. Additionally, if either of these optionsis selected, the above-described optimization objectives may beavailable to the user.

Campaign objective setting 706 may allow the user to select a desiredcampaign optimization option for the advertising campaign. For example,the user may select from one or more of: a smoothness optimizationobjective, an eCPM optimization objective, an eCPC optimizationobjective, and an eCPA optimization objective. In one embodiment, if theuser selects one of these objectives, control system 200 may apply itsrespective algorithm, discussed above, in order to control campaigndelivery and calculate the final bid price b_(ij) ^(final)(k) and finalbid allocation a_(ij) ^(final)(k) for the campaign. In addition, if theeCPA or the eCPC optimization is selected by the user, the user may alsoenter a desired target cost-per-click or conversion cost, T^(cost)(k) ,for the campaign via target eCPA/eCPC setting 708. As discussed, thisvalue represents a dollar value the user (i.e., advertiser) associateswith a consumer clicking on or converting a placed advertisement. Forexample, a user in the marketing department of an automobilemanufacturer may determine that an Internet consumer clicking orconverting one of its ads for a new automobile ad campaign is worth$50.00, based on the likelihood of the consumer becoming interested inthe automobile and eventually purchasing it. Accordingly, in thisexample, the user may set T^(cost)(k) to $50.00. Subsequently, duringoperation of the campaign, the eCPC/eCPA algorithm (whichever isselected by the user) may calculate the final bid price b_(ij)^(final)(k) for an impression from an inventory 232-236 unit based its“value” as determined by the T^(cost)(k) of $50.00 and its estimatedclick-through/conversion rate {circumflex over (p)}_(ij) ^(12C)(k)/{circumflex over (p)}_(ij) ^(12A)(k), as described above.

As shown in FIG. 10, GUI 700 may also include a bids interface 710 and acosts interface 712. Bids interface 712 may include various optionsallowing the user to set the maximum allowed bid price for animpression, r_(ij) ^(max)(k). For example, bids interface 710 mayinclude a campaign level bid price setting 714 allowing the user tospecify a desired maximum allowed bid price r_(ij) ^(max)(k) (e.g.,$2.50) for an impression, at the campaign level. If this option is used,control system 200 may use the set maximum allowed bid price r_(ij)^(max)(k) to bid on all impressions on the campaign, regardless of theparticular inventory unit 232-236 from which the impression originates.Accordingly, in one embodiment, if the user selects a desired maximumallowed bid price r_(ij) ^(max)(k)at the campaign level using campaignlevel bid price setting 714, control system 200 may use the input valueas r_(ij) ^(max)(k) when applying the various algorithms discussed abovefor impressions from all inventory units 232-236 through the course ofthe campaign.

Bids interface 710 may further include an inventory level bid pricesetting 716 allowing the user to specify distinct maximum allowed bidprices r_(ij) ^(max)(k) for impressions from different inventories232-236, i.e., at the inventory level. For example, FIG. 10 illustratesthat the user may specify a first maximum allowed bid price r_(ij)^(max)(k) (e.g., $2.95) for impressions from AOL® inventory, a secondmaximum allowed bid price r_(ij) ^(max)(k) (e.g., $2.45) for impressionsfrom Advertising.com® inventory, and a third maximum allowed bid pricer_(ij) ^(max)(k) (e.g., $2.35) for impressions from Admeld® inventory.It is to be appreciated, however, that the user may be able to specifymaximum allowed bid prices r_(ij) ^(max)(k) for impressions from anynumber of distinct ad inventories, if desired. In one embodiment, if theuser sets a desired maximum allowed bid price r_(ij) ^(max)for specificinventory (e.g., AOL) via inventory level bid price setting 714, controlsystem 200 may use the input value as r_(ij) ^(max)(k) when applying thevarious optimization algorithms discussed above, but only with respectto impressions coming from inventory units 232-236 associated with thespecified inventory (i.e., AOL).

Continuing with FIG. 10, costs interface 712 may provide informationregarding the costs associated with the advertising campaign being setup. For example, costs interface 712 may display vendor costs 718 andmanagement costs 720 associated with the advertising campaign. In oneembodiment, the vendor costs 718 may display, and/or allow the user tomodify, the agency commission fee m^(Agency) (e.g., 10%) charged by thead network for the campaign. Additionally, vendor costs 718 may display,and/or allow the user to modify, the external data fee r^(edf) (e.g.,$0.50 per impression) for the campaign.

Management costs 720 may include, for example, the management feem^(Aol) 1 the user (i.e., advertiser) is being charged per impressionfor enjoying the benefit of using the disclosed campaign managementservices. In addition, management costs 720 may include the internaldata fee r^(idf) (e.g., $0.55 per impression) the user (i.e.,advertiser) is being charged per impression during the campaign.

GUI 700 may further include a launch campaign Ul element 722. When theuser selects this element, the various campaign settings 702-708, 714,716, 718 the user entered via GUI 700 may be provided as campaignconfiguration information 222 (FIG. 4) to control system 200.Subsequently, control system 200 may control the campaign based on thecampaign configuration information 222, as described above.

The disclosed systems and methods may improve the performance of onlineadvertising campaigns. For example, it is to be appreciated that thedisclosed techniques may provide a user (e.g., advertiser) with greatflexibility to customize an online advertising campaign in a way thatnot only meets the ad network's revenue goals, but also reduces thecost, and increases the performance, of the campaign from theperspective of the user. In particular, as discussed, the user ispermitted to select from among a plurality of different campaignoptimization techniques (e.g., smoothness, eCPM, eCPC, and eCPA) thatcan reduce the cost of the campaign and improve its performance.Specifically, as described, the disclosed smoothness and eCPM objectiveseach allows the user to set a desired a maximum allowed cost forimpression r_(ij) ^(max)(k), either at the campaign level or at theinventory level, which is used to control campaign delivery. As aresult, when using these objectives, the average cost per impression canbe optimized or reduced in favor of the user.

Similarly, the disclosed eCPC/eCPA objectives also each allows the userto set a desired a maximum permitted cost for impression r_(ij)^(max)(k), either at the campaign level or at the inventory level, whichis used to control campaign delivery. In addition to considering thismaximum allowed cost for impression r_(ij) ^(max)(k), the eCPC/eCPAobjective also allows the user to specify a targetcost-per-click/conversion T^(cost)(k). As discussed, the disclosedalgorithms use this target cost-per-click/conversion T^(cost)(k) and theestimated performance of inventory units, as represented by itsestimated click-through/conversion rate {circumflex over (p)}_(ij)^(12C)(k)/{circumflex over (p)}_(ij) ^(12A)(k), to determine aperformance “value” for the inventory. The eCPC/eCPA objectives thencalculate the final bid price b_(ij) ^(final)(k) based on whichever isless between the maximum allowed bid for impression r_(ij) ^(max)(k) andthe calculated performance value of the inventory unit. Accordingly, inthe disclosed eCPC/eCPA optimization algorithms, the user may not paymore for an impression than what the user has specified it is worth interms of its click-through/conversion rate. Additionally, the disclosedalgorithms may “cap” the final bid price b_(ij) ^(final)(k) based onwhat the user has specified as the maximum allowed cost for impressionr_(ij) ^(max)(k).

The disclosed techniques may also provide improved transparency andcontrol to the user (i.e., advertiser) in terms of how an onlineadvertising campaign is operated. For example, as described above, theuser has access to the cost information 712 associated with thecampaign, via GUI 700. Also, as discussed, the user has the flexibilityto specify maximum allowed bid for impression r_(ij) ^(max)(k) at thecampaign level and/or inventory level. Accordingly, in comparison toconventional advertising techniques in which the user is not aware ofthe hidden costs and fees associated with running a campaign, and doesnot have such granular control over how the advertising budget is spent,the disclosed techniques can offer improved transparency and control tothe user.

One skilled in the art will appreciate that computer programs forimplementing the methods of may be stored on and/or read fromcomputer-readable storage media. The computer-readable storage media mayhave stored thereon computer-executable instructions which, whenexecuted by a computer (e.g., one or more components of system 100),cause the computer to perform, among other things, the processesdisclosed herein. Exemplary computer-readable storage media may includemagnetic storage devices, such as a hard disk, a floppy disk, magnetictape, or another magnetic storage device known in the art; opticalstorage devices, such as CD-ROM, DVD-ROM, or another optical storagedevice known in the art; and/or electronic storage devices, such asEPROM, a flash drive, or another integrated circuit storage device knownin the art. The computer-readable storage media may be embodied by or inone or more components of system 100 (FIG. 1).

The many features and advantages of the disclosure are apparent from thedetailed specification, and thus, it is intended by the appended claimsto cover all such features and advantages of the disclosure which fallwithin the spirit and scope of the disclosure. Further, since numerousmodifications and variations will readily occur to those skilled in theart, it is not desired to limit the disclosure to the exact constructionand operation illustrated and described, and accordingly, all suitablemodifications and equivalents may be resorted to, falling within thescope of the disclosure.

1-25. (canceled).
 26. A control system for controlling an onlineadvertising campaign, the control system comprising: at least one firstprocessor configured to: provide a feedback signal reflecting at leastone real-time delivery metric associated with an advertising deliverygoal for a current sampling period regarding one or more impressions ofthe online advertising campaign at one or more inventory units, the atleast one real-time delivery metric including ad events associated withthe inventory units; and generate a delivery reference signal particularto each of the one or more inventory units based on the feedback signaland configuration information for the online advertising campaign; atleast one second processor, associated with each inventory unit of theone or more inventory units, configured to compensate the receivedfeedback signal based on a day of the week and time of the day accordingto a moving sum of the feedback signal over a predetermined timeinterval, normalize the compensated feedback signal and the deliveryreference signal based on an estimated gain value of the inventory unitand a control signal from a prior sampling period, compare thenormalized compensated feedback signal to the normalized deliveryreference signal for the inventory unit, and generate a campaign levelcontrol signal for the inventory unit based on the comparison; and atleast one third processor, associated with each inventory unit,configured to: receive a maximum impression bid price for the inventoryunit of the online advertising campaign; receive an optimizationobjective for the online advertising campaign; calculate at least afinal bid price based on the maximum impression bid price, the campaignlevel control signal received from the at least one second processor,and the optimization objective for the online advertising campaign; andsubmit, to an electronic market and based on the calculated final bidprice, a bid on an impression at the inventory unit.
 27. The controlsystem of claim 26, wherein the at least one third processor is furtherconfigured to: calculate a bid allocation based on the optimizationobjective, on the maximum bid price, and on the campaign level controlsignal; and submit the bid based further on the calculated bidallocation.
 28. The control system of claim 26, wherein the optimizationobjective is further selected from one of a reduced average cost perclick, a reduced average cost per conversion, and a revenue smoothnessobjective, each of which is measured over the course of the onlineadvertising campaign.
 29. The control system of claim 26, wherein the adevent includes an ad click event, and the ad event rate includes an adclick-through rate.
 30. The control system of claim 26, wherein the adevent includes an ad conversion event, and the ad event rate includes anad conversion rate.
 31. The control system of claim 26, wherein the atleast one third processor is configured to calculate a performance valueby multiplying a ad event rate and a received target cost-per-ad eventset by the user.
 32. The control system of claim 26, wherein tocalculate the final bid price, the at least one third processor isconfigured to: determine which is less between the maximum impressionbid price and the performance value for the inventory unit; andcalculate the final bid price based on the lesser of the maximumimpression bid price and the performance value for the inventory unit.33. The control system of claim 26, further comprising a graphical userinterface configured to allow the user to input the maximum impressionbid price and to select the optimization objective.
 34. The controlsystem of claim 33, wherein the graphical user interface allows the userto specify the maximum impression bid price for at least one of theentire online advertising campaign and just the inventory unit.
 35. Thecontrol system of claim 33, wherein the graphical user interfacedisplays to the user cost information associated with the advertisingcampaign, the cost information including at least one of an agencycommission fee, an external data fee, a management fee, or an internaldata fee associated with bidding on impressions at the inventory unit.36. A computer-implemented method for controlling an online advertisingcampaign, the computer-implemented method comprising: receiving, by afirst processor associated with an inventory unit of one or moreinventory units, a feedback signal reflecting at least one real-timedelivery metric associated with an advertising delivery goal for acurrent sampling period regarding one or more impressions of the onlineadvertising campaign at the one or more inventory units; receiving, bythe first processor, a delivery reference signal particular to theinventory unit, wherein the delivery reference signal comprisesconfiguration information for the online advertising campaign;compensating, by the first processor, the received feedback signal basedon a day of the week and time of the day according to a moving sum ofthe feedback signal over a predetermined time interval, the firstprocessor further configured to normalize the compensated feedbacksignal and the delivery reference signal based on an estimated gainvalue of the inventory unit and a control signal from a prior samplingperiod; comparing the normalized compensated feedback signal to thenormalized delivery reference signal for the inventory unit associatedwith the first processor to generate a campaign level control signal forthe inventory unit; receiving, by a second processor associated with theinventory unit, information identifying ad events associated with theinventory unit, a maximum impression bid price for the inventory unit ofthe online advertising campaign, an optimization objective for theonline advertising campaign, and a target cost-per-ad event for theinventory unit, wherein the maximum bid price for the inventory unit,the optimization objective, and the target cost-per-ad event are eachset by the user, and wherein the optimization objective includes areduced average cost per impression over the course of the onlineadvertising campaign; calculating, using the second processor, at leasta final bid price based on the maximum bid price, the campaign levelcontrol signal, and the optimization objective for the onlineadvertising campaign; and submitting, to an electronic market and basedon the calculated final bid price, a bid on an impression at theinventory unit.
 37. The computer-controlled method of claim 36, furthercomprising: calculating a bid allocation based on the optimizationobjective, on the maximum bid price, and on the campaign level controlsignal; and submitting the bid based further on the calculated bidallocation.
 38. The computer-controlled method of claim 36, wherein theoptimization objective is further selected from one of a reduced averagecost per click, a reduced average cost per conversion, and a revenuesmoothness objective, each of which is measured over the course of theonline advertising campaign.
 39. The computer-controlled method of claim36, wherein the ad event includes an ad click event, and the ad eventrate includes an ad click-through rate.
 40. The computer-controlledmethod of claim 36, wherein the ad event includes an ad conversion eventand the ad event rate includes an ad conversion rate.
 41. Thecomputer-controlled method of claim 36, further comprising calculatingthe performance value by multiplying the ad event rate and the receivedtarget cost-per-ad event set by the user.
 42. The computer-controlledmethod of claim 36, wherein calculating the final bid price includes:determining which is less between the maximum impression bid price andthe performance value for the inventory unit; and calculating the finalbid price based on the lesser of the maximum impression bid price andthe performance value for the inventory unit.
 43. Thecomputer-controlled method of claim 36, further comprising providing agraphical user interface allowing the user to input the maximumimpression bid price and to select the optimization objective.
 44. Thecomputer-controlled method of claim 43, further comprising displaying tothe user, via the graphical user interface, cost information associatedwith the advertising campaign, the cost information including at leastone of an agency commission fee, an external data fee, a management fee,and an internal data fee associated with bidding on impressions at theinventory unit.
 45. The computer-controlled method of claim 43, whereinthe graphical user interface allows the user to specify the maximumimpression bid price for at least one of the entire online advertisingcampaign and just the inventory unit.